Ex-ante assessment of different vaccination-based control schedules against the peste des petits ruminants virus in sub-Saharan Africa

Pachka Hammami; Renaud Lancelot; Joseph Domenech; Matthieu Lesnoff

doi:10.1371/journal.pone.0190296

Abstract

Background

Peste des petits ruminants (PPR) is a highly contagious and widespread viral infection of small ruminants (goats and sheep), causing heavy economic losses in many developing countries. Therefore, its progressive control and global eradication by 2030 was defined as a priority by international organizations addressing animal health. The control phase of the global strategy is based on mass vaccination of small ruminant populations in endemic regions or countries. It is estimated that a 70% post-vaccination immunity rate (PVIR) is needed in a given epidemiological unit to prevent PPR virus spread. However, implementing mass vaccination is difficult and costly in smallholder farming systems with scattered livestock and limited facilities. Regarding this, controlling PPR is a special challenge in sub-Saharan Africa. In this study, we focused on this region to assess the effect of several variables of PVIR in two contrasted smallholder farming systems.

Methods

Using a seasonal matrix population model of PVIR, we estimated its decay in goats reared in sub-humid areas, and sheep reared in semi-arid areas, over a 4-year vaccination program. Assuming immunologically naive and PPR-free epidemiological unit, we assessed the ability of different vaccination scenarios to reach the 70% PVIR throughout the program. The tested scenarios differed in i) their overall schedule, ii) their delivery month and iii) their vaccination coverage.

Results

In sheep reared in semi-arid areas, the vaccination month did affect the PVIR decay though it did not in goats in humid regions. In both cases, our study highlighted i) the importance of targeting the whole eligible population at least during the two first years of the vaccination program and ii) the importance of reaching a vaccination coverage as high as 80% of this population. This study confirmed the relevance of the vaccination schedules recommended by international organizations.

Citation: Hammami P, Lancelot R, Domenech J, Lesnoff M (2018) Ex-ante assessment of different vaccination-based control schedules against the peste des petits ruminants virus in sub-Saharan Africa. PLoS ONE 13(1): e0190296. https://doi.org/10.1371/journal.pone.0190296

Editor: Nagendra R. Hegde, National Institute of Animal Biotechnology, INDIA

Received: February 15, 2017; Accepted: December 12, 2017; Published: January 19, 2018

Copyright: © 2018 Hammami et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting Information files.

Funding: This study was conducted in the frame of the project “Vaccine Standard and Pilot Approach for the Progressive Control and Eradication of PPR in Africa” (VSPA), funded by the Bill and Melinda Gates Foundation, the OIE World Animal Health and Welfare Fund, and Cirad. Small ruminant demographic data were collected during the joint research programme “Pathologie et Productivité des Petits Ruminants” co-funded by the Institut Sénégalais de Recherches Agricoles (Isra, Dakar, Senegal), the Directorate of Veterinary Services (DSV, Dakar, Senegal), and Cirad. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Peste des petits ruminants (PPR) is an acute viral disease affecting goats, sheep and some wild ruminant species. It is caused by a Morbillivirus from the Paramyxoviridae family. The PPR virus (PPRV) is widespread in Africa, the Middle East and Asia [1]. When introduced in a fully susceptible population, it can affect up to 100% of the individuals, killing from 10 to 90% of the infected animals [2]. Therefore, the introduction of PPRV in previously free areas, as well as its endemicity in many developing countries, result in severe consequences for food security and sustainable livelihood of livestock farming communities. Such a situation exists in sub-Saharan Africa where the poor rural communities rely on small-ruminant farming and hence PPR control is of crucial importance for millions of people living in this area [3–6].

In 2015, the World Organisation for Animal Health (OIE) and the Food and Agriculture Organization of the United Nations (FAO) launched an international initiative for the progressive control of PPR and its global eradication by 2030. As a matter of fact, efficient and affordable vaccines, which provide lifelong immunity against PPRV are available [7–9]. Therefore, relying on the lessons learnt from the successful eradication of rinderpest in 2008 [10], the general PPR control strategy is based on mass vaccination of the whole small-ruminant population in endemic countries or regions [11].

The effectiveness of PPR control strategies depends on numerous factors, such as the quality of the vaccine itself, the design of vaccination campaigns (e.g., the vaccination schedule or the definition of the target population), the effectiveness of vaccine delivery (e.g., the maintenance of the cold chain or the vaccination coverage reached in the target population) or the willingness of farmers to present their animals for vaccination and to bear the cost of this vaccination [12]. However, specific data regarding PPR mass vaccination are scarce, making it difficult to plan and organize vaccination campaigns [13–15].

Following a successful pulse vaccination campaign (whole target population vaccinated within a short time period), the proportion of immunized animals in a small ruminant population (so-called post-vaccination immunity rate: PVIR) must be high enough to stop the PPRV transmission, thus bringing it under the epidemic threshold [16]. Because of the population turnover (offtake, mortality and birth), the immunized animals are progressively replaced with susceptible animals (newborn, purchased animals, loans) until the epidemic threshold has passed. Therefore, PVIR dynamics are closely related to this turnover and can be estimated using a population dynamics model [17, 18]. In a previous study, based on a predictive model of PVIR dynamics developed in Lesnoff et al. (2009) [17], the monthly PVIR dynamics was estimated for an average year following a PPR vaccination campaign in a sheep population reared in a semi-arid area [18]. In this latter study, a PVIR threshold of 70% was used: populations with a PVIR ≥ 70% were considered as protected against PPR virus transmission, according to international standards [11].

However, a wider range of agro-ecosystems is found in sub-Saharan Africa—from arid to humid environments, with contrasted small-ruminant population dynamics. In addition, a crucial step in the PPR eradication strategy is the control stage, aiming at breaking the PPRV transmission process and diminishing drastically or even suppressing the number of PPR clinical cases [11]. This control stage relies on a pluri-annual mass-vaccination schedule, associated with post-vaccination evaluation to assess the population immune status and the reduction in PPR clinical incidence.

The OIE/FAO recommended vaccination schedule involves one or two annual vaccination campaigns targeting all immunocompetent animals i.e., older than three months. These “full” campaigns may be followed by one or two annual “partial” vaccination campaigns targeting the immunocompetent offspring (age between three and 12 months), i.e. excluding the adults [11]). However, this recommendation relies more on empirical observations than on a rational assessment.

The goal of this study is to provide an ex ante assessment of the PVIR dynamics in different small ruminant smallholder farming systems, and over a pluri-annual PPR vaccination schedule.

For this purpose, we simulated the PVIR dynamics according to different four-year mass-vaccination schedules, in immunologically naive and PPR-free epidemiological units (Epi. U.) from contrasting agro-ecological situations: a semi-arid area where the population dynamics is highly seasonal because of climate-related nutritional constraints [19], and a sub-humid area where the population turnover is fairly constant—but faster due to higher mortality and reproduction rates [20]. As an indication of the importance of these systems, according to FAO’s database Gridded livestock of the world [21], and considering the countries located in the Sahelian region (Mauritania, Senegal, Gambia, Mali, Burkina Faso, Niger, Chad, and Sudan), more than 100 million small ruminants are reared in semi-arid or sub-humid agro-ecological areas.

For simplicity, and consistency with assumptions and findings made in Hammami et al., 2016 [18], the Epi. U. were defined as isolated populations of a few thousand small ruminants with no PPRV transmission occurring during the whole study period. As a matter of fact, in Senegal as in most Sahelian countries, agro-pastoral populations are organized in rural communities gathering several villages sharing the environment (grasslands, crop lands, water resources), and services like health centres or veterinary posts. These communities and their herds have more contacts and exchanges within them than between them. Therefore, considering this organization level as an Epi. U. makes sense in the frame of this study. At last, though PPR is endemic in this region, epidemic waves occur every two to five years [1, 22], making it relevant to consider PPR-free Epi. U., and the implementation of preventive vaccination during PPR-free years.

Materials and methods

The Epi. U.-level monthly population dynamics was simulated using a seasonal population matrix model described in Caswell, 2001 (see chapter 13, pp. 346-369) [23]. The basic demographic model and the PVIR estimation method were described for one average year in Hammami et al. (2016) [18]. Following the international recommendations [11], we considered (a) different four-year vaccination schedules according to the sequence of full annual campaigns (targeting all immunocompetent animals) and partial annual campaigns (only targeting immunocompetent offspring), and (b) two possible PVIR variables: i) the vaccination month and ii) the vaccination coverage in the target population.

Small ruminant farming systems

To estimate the demographic parameters needed in the seasonal population matrix model, we used sheep-and-goat demographic data collected during a long-term follow-up survey implemented in smallholder farms from 1983 to 1999 [19, 20, 24]. To represent the diversity of agro-ecological situations and associated small-ruminant farming systems found in sub-Saharan Africa, we selected sheep data from the Ndiagne municipality, located in the Sahelian, semi-arid zone (Louga region, northern Senegal) and goat data from Kolda, in the Sudano-Guinean, sub-humid area of southern Senegal (Fig 1). These low-input farming systems rely on the utilization of natural grasslands. Consequently, animal breeds, as well as animal demography and productivity, are closely related to climatic and forage conditions [19, 20].

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Fig 1. Distribution of aridity classes in Senegal.

The small-ruminant follow-up demographic surveys were located in the Ndiagne municipality (Louga region) and Kolda area. This map was adapted from Hammami et al., 2016 [18] under a CC BY license, with permission from Dianne Cartwright—PLoS ONE, original copyright 2016. It was generated using data sources from Zomer et al., 2006 [25] and Trabucco et al., 2009 [26]; spatial resolution: 10 arc minutes.

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In semi-arid areas, such as Louga region, the harsh climatic conditions experienced by the small ruminants (Sahelian breeds) during the hot, dry season result in severe constraints on their nutritional condition and physiological status. Therefore, mating is strongly seasonal, mostly occurring during the rainy season. Thus, there is a marked parturition peak between December and February [19, 27, 28] (Fig 2-A).

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Fig 2. Demographic rates in Louga sheep and Kolda goats.

Upper plots (A and B) show the monthly parturition rate for females older than 10 months, and the annual prolificacy rates. The lower plots show the natural mortality rates (without offtake) for three age classes.

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Conversely, in the sub-humid area of Kolda, forage resources are less scarce and available throughout the year. Consequently, mating in small ruminants (West African dwarf breeds) is less constrained than in the Sahelian zone, and parturition peaks are less marked (Fig 2-A). Also, fertility and prolificacy rates are higher, resulting in better fecundity [20, 27] (Fig 2-B). Such environment is also more suitable to parasites [29]. Therefore mortality rates are higher than in semi-arid areas (Fig 2-C). In both sites, the male offtake rate is high and strongly seasonal according to the Tabaski (Aïd El Kebir), a Muslim celebration during which a male lamb is slaughtered in most Senegalese families. However, this offtake pattern does not affect the PVIR dynamics [18]. Therefore, we did not account for it in this assessment.

Design for the PVIR assessment

From hyper-arid to humid areas, the same general vaccination schedule is recommended for the PPR control stage [11]. In regions where PPR is endemic like in sub-Saharan Africa, it relies on the implementation of one or two successive annual mass-vaccination campaigns targeting all immunocompetent animals, i.e. sheep and goats older than three months: so-called full vaccination, hereafter denoted as “F”. These full vaccination campaigns may be complimented by one or two partial annual campaigns, hereafter denoted as “P”, targeting only the immunocompetent offspring, i.e. lambs or kids between three and 12 months.

Whatever the schedule, in arid and semi-arid areas, a single vaccination round per annual campaign is recommended at the beginning of the dry season (from September to November) [11, 18], i.e. before the parturition peak so that newborn kids and lambs can benefit from their dam’s colostral antibodies.
In sub-humid and humid areas, two rounds of vaccination (every six months) are recommended for each annual campaign to account for the quicker demographic turnover than in the arid and semi-arid environments [11].

The main question was the effect of different vaccination schedules (combination of full and partial vaccination campaigns) on PVIR during the PPR control stage. Following international recommendations, the length of this stage should range from two to five years, with an average of three years: see [11]. Therefore, we assessed the effect of one to three full annual vaccination campaigns (1F, 2F and 3F), complimented by partial annual vaccination campaigns (3P, 2P and 1P) up to a total of four years arbitrarily set as the length of the PPR control stage. The compared vaccination schedules were thus 1F3P, 2F2P, and 3F1P, for a total of four vaccination rounds in Louga (one per year), vs. eight in Kolda (two per year).

In addition, the effect of two other factors was assessed:

the vaccination month: following previous findings [18], three vaccination months were compared in Louga: September, October, and November. In Kolda, this factor did not affect PVIR, so it was not taken into account (see preliminary results in supporting information S1 Fig).
partial vaccination coverage, to account for possible difficulties in vaccine delivery (e.g., vaccination logistics, reluctance of farmers to bring their animals for vaccination): we assessed four vaccination coverages: 30%, 60%, 80%, and 100%.

Assessment method

Monthly PVIR dynamics was simulated over a four-year control period using as input i) the simulated population dynamics and ii) the vaccination scenarios defined by the combination of vaccination schedule (1F3P, 2F2P and 3F1P), vaccination month (only in Louga: September, October and November) and vaccination coverage (30%, 60%, 80%, and 100%).

The seasonal population matrix model and the PVIR estimation for one average year have been previously described [18]. In this study, the PVIR estimation method was slightly modified to implement additional vaccination campaigns and target different subsets of the population. It was based on two main assumptions:

for immunocompetent animals, given the lifelong immunity provided by the vaccine [7, 30], the probability for a given cohort to be immunized was constant between two vaccination rounds (a cohort represented all the animals born during the same month: see the yellow band on Fig 3 for an example);
colostral antibodies against PPR were found in kids and lambs during the first three months of their life. This passive immunity waned and disappeared after the age of three months [31–34]. The proportion of lambs benefiting from those antibodies was proportional to dams’ immunity rate (see the shades of green in Fig 3). Moreover, the immune system of lambs under three months of age is immature and then unable to produce an efficient immune response to the vaccine inoculation [35].

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Fig 3. Theoretical population immunity rate dynamics over two years with annual vaccination campaigns, illustration adapted from Hammami et al., 2016 [18].

For simplicity, only animals up to two-year old were shown.

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Demographic rates used in the demographic model were either natural (survival, parturition and prolificacy rates), or related to livestock management practices (offtake and intake rates). Using generalized linear models, their means and standard errors were estimated for each age class (newborn, sub-adults and adults) and time step (four-month seasons: January to March, etc.) for each species and site (sheep in Louga, goats in Kolda). Assuming farmers annually targeted a constant herd size, female offtake rates were adjusted to arrive at population dynamics at the equilibrium.

To assess the uncertainty of statistical model predictions regarding the demographic parameters, 10,000 simulations were run for each scenario using random demographic rates drawn from the estimated Gaussian distribution of the demographic rates [36].

Data analysis

In this study, we were confronted with a huge amount of data. For instance, the simulated Louga sheep dataset encompassed three vaccination schedules × three vaccination months × four vaccination coverages × 10,000 simulations = 360,000 PVIR dynamics, which means 360,000 × 48 months = 1,728,000 data points. In this situation, any statistical test would provide very small p value, whatever the actual size of the epidemiological effect [37]. Therefore, we defined an ad-hoc procedure to assess the epidemiological significance of the investigated parameters (vaccination schedule, vaccination month and vaccination coverage).

We defined 2F2P as the reference vaccination schedule for each site (Louga and Kolda), i.e., (i) two full annual mass-vaccination campaigns (2F) targeting the whole immunocompetent population followed by (ii) two partial annual mass-vaccination campaigns targeting the immunocompetent offspring (2P); to account for the local circumstance of small ruminant population dynamics, each annual vaccination campaign was made of one (Louga) or two (Kola) vaccination rounds. Therefore, in Louga, the reference vaccination schedule was defined by one vaccination round every 12 months, while, in Kolda, it was defined by one vaccination round every six months.

Then, we estimated the four-year PVIR dynamics for these two regions (Louga and Kolda), according to the other investigated variables: vaccination month and vaccination coverage.

For each combination of the variables, we computed the 50% (median), as well as the 2.5% and 97.5% (95% distribution interval) quantiles for three statistics (θ_i) summarizing the dynamics of PVIR:

T_thr was the proportion of time during which the PVIR was above the 70% population immunity threshold during the four years of the PPR control stage, i.e. during which the population was protected against virus transmission. In practice, we counted the number of months during which the PVIR was higher than 70% and divided this number by the length of the program: 48 months. For example, if the PVIR remains above 70% during seven months, T_thr = 7/48 = 15%;
M_PVIR was the mean PVIR over the PPR control stage;
PVIR₄₈ was the PVIR at the end of the PPR control stage.

We implemented the same analysis for the other investigated vaccination schedules (1F3P and 3F2P).

Thirdly, for each statistics (θ_i = {T_thr, M_PVIR, PVIR₄₈}), we computed the relative difference (Δ_i,j) between the reference schedule and the others for each combination of variables as: with θ_i,ref the statistics for the reference vaccination schedule 2F2P, and j the vaccination schedule to be compared with the reference schedule: 1F3P and 3F1P.

Finally, we defined an epidemiological interval around Δ_i, arbitrarily set to Δ_i ± 7%. This interval represented a 5% relative difference for the 70% immunity threshold (5% ≃ 70% × 7%). The rationale of this choice is that in most PPR sero-monitoring surveys implemented to assess the post-vaccination PVIR, survey design and actual sample size usually provide confidence intervals of similar order: see the on-line appendix in [11].

Results

Overview

An overview of the results is provided in Table 1 for Louga sheep, and in Table 2 for Kolda goats.

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Table 1. Distribution of indicators of PVIR according to the PPR vaccination scenarios (combination of vaccination schedule, month, and coverage (%)) for a sheep population in Louga, northern Senegal.

A total of 10,000 simulations were run for each scenario. Q02.5: quantile 2.5%, Q97.5: quantile 97.5%; T_thr: time spent above the 70% threshold; M_PVIR: mean PVIR over the PPR control stage; PVIR₄₈: PVIR at the end of the PPR control stage.

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Table 2. Distribution of indicators of PVIR according to the PPR vaccination scenarios (combination of vaccination schedule and coverage (%)) for a goat population in Kolda, southern Senegal.

A total of 10,000 simulations were run for each scenario. Q02.5: quantile 2.5%, Q97.5: quantile 97.5%; T_thr: time spent above the 70% threshold; M_PVIR: mean PVIR over the PPR control stage; PVIR₄₈: PVIR at the end of the PPR control stage.

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In Louga sheep, a vaccination coverage of 30% never allowed reaching, or getting close to, the 70% population immunity threshold, whatever the vaccination month and vaccination schedule: indeed, the cumulative effect of successive vaccination campaigns on PVIR was not high enough to compensate the sheep population turnover. A vaccination coverage of 60% did not allow reaching this threshold with the 1F3P schedule; however, the median value for the estimated mean PVIR (M_PVIR) was close to, or above, 60% for 2F2P and 3F1P. Regarding the immunity rate at the end of the control stage (PVIR₄₈), it was close to, or above, 50% irrespective of the vaccination schedule and vaccination month. Logically, better values were obtained with 80% and 100% vaccination coverages. However, even with the 3F1P schedule and a full vaccination coverage, the median value for M_PVIR was never greater than 80%, and the median value for PVIR₄₈ just exceeded 60%.

Regarding the vaccination month, the best values for the investigated indicators were obtained in September, with the exception of PVIR₄₈ for which the highest median values were reached in October and November. However the differences in PVIR₄₈ between the three vaccination months were low, without any epidemiological consequence.

In Kolda goats, similar trends were observed: a 30% vaccination coverage never allowed reaching values close to the targets for any of the three indicators, with the same result as with Louga sheep regarding the lack of cumulative effect along the control stage irrespective of the vaccination schedule. As soon as the vaccination coverage was 60% or higher, median values greater than 60% were reached for M_PVIR and PVIR₄₈. However, vaccination coverages ≥ 80% and at least two full vaccination campaigns were needed to reach values of T_thr > 60%.

In fine, a vaccination coverage of 30% or lower was certainly insufficient to get an appropriate PVIR, whatever the agro-ecological zone and vaccination schedule. Also, a single full vaccination campaign (1F3P) only provided correct PVIR for vaccination coverages of at least 80%.

The reference vaccination schedule 2F2P

In both investigated agro-ecological systems, with a full vaccination coverage (100% fo the immunocompetent population), the 2F2P vaccination schedule allowed keeping the PVIR above, or close to, the 70% population immunity threshold during the PPR control stage (Fig 4). In Louga sheep (OviLou: a single vaccination round each year), the PVIR was above this threshold 67% of the time, declining below it eight months after each vaccination round. In Kolda goats (CapKol: two vaccination rounds each year), the PVIR was always above the threshold during the control stage. However, the PVIR estimated immediately after the vaccination round was higher in OviLou (96%, 95% confidence interval [95; 96]) than in CapKol (81% [80; 82]).

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Fig 4. PVIR dynamics during a four-year control stage (2F2P schedule) in a sheep population from Louga (northern Senegal: Red line) and in a goat population from Kolda (southern Senegal: Blue line), with different vaccination coverages and the first vaccination round implemented in October.

The vaccination coverages represent the proportion of vaccinated animals among the immunocompetent animals (> 3 months). Each line represents the PVIR for the whole population. The 70% PVIR threshold is represented by a black dotted line.

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With vaccination coverages of > 80%, these PVIR results should allow reaching the goals assigned to the PPR control stage: break PPRV transmission and suppress PPR clinical expression. Therefore, the data support the relevance of the 2F2P vaccination schedule as a reference for comparisons with other schedules.

Relative difference with respect to the 2F2P vaccination schedule

In Louga sheep, the combined effects of the vaccination month and the vaccination coverage is shown on Figs 5 to 7. A common pattern is the low effect of the vaccination month. We did not investigate this effect further.

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Fig 5. Relative difference in time spent above the 70% threshold (T_thr) for post-vaccination PVIR with respect to the 2F2P vaccination schedule for Louga sheep, northern Senegal.

The red, solid line indicates the reference situation (2F2P), and the red, dashed lines above and under it indicate a positive or negative relative 7%-difference with this reference situation.

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Fig 6. Relative difference in the mean PVIR (M_PVIR) with respect to the 2F2P vaccination schedule for Louga sheep, northern Senegal.

The red, solid line indicates the reference situation (2F2P), and the red, dashed lines above and under it indicate a positive or negative relative 7%-difference with this reference situation.

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Fig 7. Relative difference in the PVIR at the end of the PPR control stage (PVIR₄₈) with respect to the 2F2P vaccination schedule for Louga sheep, northern Senegal.

The red, solid line indicates the reference situation (2F2P), and the red, dashed lines above and under it indicate a positive or negative relative 7%-difference with the reference situation.

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For the time spent with PVIR ≥ 70% (T_thr, Fig 5), the 60% vaccination coverage provided a positive relative difference for the 3F1P vaccination schedule (upper panel of plots) with a substantial epidemiological meaning, in favour of 3F1P. This can be seen as a cumulative effect along the PPR control stage, made possible by three successive full vaccination campaigns. This effect disappeared with higher vaccination coverage: the immunity rate was close to its maximum value after each annual vaccination campaign, thus nullifying the possibility of a cumulative effect.

Not surprisingly, a symmetrical negative relative difference was observed for the 1F3P vaccination schedule (lower panel of plots), indicating that this schedule was consistently worse than the 2F2P schedule in terms of PVIR, with the exception of the 100% vaccination coverage, for which the relative difference was not epidemiologically important.

For the relative difference in the mean PVIR (M_PVIR, Fig 6), all the values fell within the 7% epidemiological interval with the exception of the relative difference for the 60% vaccination coverage with the 1F3P vaccination schedule. We can conclude that for this indicator, there was no important epidemiological difference between the 2F2P and 3F1P vaccination schedules when vaccination coverage was ≥ 80%.

The same observations applied to the statistics of the PVIR at the end of the PPR control stage (PVIR₄₈, Fig 7).

To sum up the findings for Louga sheep, the 60% vaccination coverage consistently provided worse relative indicators than 80% and 100% coverages, supporting the implementation of three full vaccination campaigns rather than one or two. On the other hand, with the latter coverages, no marked difference was observed between the 1F3P and 3F1P vaccination schedules, with respect to 2F2P.

Regarding the Kolda goats (Fig 8), similar conclusions can be made. A cumulative effect of successive vaccination campaigns was observed for the relative difference in T_thr with the 30% as well as 60% vaccination coverages. On the other hand, no marked difference was observed between the 1F3P and 3F1P vaccination schedules, with respect to 2F2P, for the two other indicators (M_PVIR and PVIR₄₈) with vaccination coverages ≥ 60%.

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Fig 8. Relative difference in indicators of post-vaccination PVIR with respect to the 2F2P vaccination schedule for Kolda goats, southern Senegal.

The red, solid line indicates the reference situation (2F2P), and the red, dashed lines above and under it indicate a positive or negative relative 7%-difference with the reference situation.

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Discussion

Validity of the small ruminant demographic dataset

Demographic data were collected in smallholder, low-input small ruminant farming systems, the most challenging systems for PPR vaccination, because herds are small, sparse, mobile and therefore difficult to reach and monitor. These systems rely on the availability of natural resources: surface water during the rainy season, grasslands, and crop by-products (in the southern regions). Small ruminant demography (mortality, age at first parturition, fecundity) and growth strongly depend on these conditions [28, 38]. However, when these conditions are better (higher rainfall), more room is given by the farmers to crop production, whereas ruminant livestock farming is left to more arid environment. Therefore, though such climate changes occurred in Senegal and in the whole Sahel with increased rainfall and vegetation index [39], the demographic parameters of small ruminants reared in low-input smallholder farming systems probably remained stable. Obviously, formal evidences would be better than assumptions, but the dataset we have used for this paper has no up-to-date counterpart. Thus, this study highlights the need for implementing more long-term follow-up demographic studies in small ruminant population to increase the accuracy of such demographic models.

Vaccination month

The effectiveness of a vaccination campaign is directly related to the actual vaccination coverage. If the population is mainly composed of immunocompetent animals at the time of vaccination, the PVIR is higher than if the vaccination campaign is achieved just after the parturition peak (when present) [18]. Indeed, newborn animals benefit from maternal antibodies (passive immunity). This immunity wanes after the age of three months, and offspring thus become susceptible to the virus. However, their immune system is then mature enough to produce antibodies [33, 40].

Therefore, when the small ruminant reproduction shows a marked seasonal pattern, like in arid and semi-arid areas, the ideal period for implementing the vaccination would be three months after the parturition peak, i.e. between April and June in the case of Louga [18, 27, 28]. However, this period is also the beginning of the hot, dry season. At that time, farmers lack financial resources to pay for the vaccination because they already sold their crops [19, 41]. Moreover, sheep and goats are left straying at that time, and farmers—as well as vaccinators, are reluctant to catch and gather them when air temperature often exceeds 40°C. At last, most animals are in a poor body condition, thus possibly affecting their immune system and causing vaccination failure at the individual level, even if the vaccine was correctly administered [42, 43]. Therefore, in arid and semi-arid areas, the best period to implement PPR vaccination should be from September (end of the rainy season, when body condition is optimal) and November (before the parturition peak, to make sure that dams may transmit colostral antibodies to their offspring). Estimates of the median indicators (Table 1: T_thr, M_PVIR, and PVIR₄₈) consistently showed that September was the best month to implement vaccination. This trend was not altered by the vaccination coverage (Figs 5 to 7).

Vaccination coverage and vaccination schedule

Three major features were highlighted by our results:

Vaccination coverage must be > 60% to reach the 70% PVIR threshold (M_PVIR indicator), whatever the vaccination month (Louga sheep) and site (Louga vs. Kolda). However in Kolda goats, the 3F1P schedule associated with a 60% vaccination coverage brought M_PVIR very close to the threshold, in the context of two vaccination rounds per annual campaign.
When the vaccination coverage decreased, the number of full vaccination campaigns had to be higher to reach the 70% PVIR threshold.
Symmetrically, when the number of full vaccination campaigns was higher, the PVIR were better, whatever the vaccination month (Louga) and PVIR indicator.

Good results were obtained in Kolda with the 1F3P schedule and 60% vaccination coverage: M_PVIR and PVIR₄₈ were both above the 70% threshold. This is certainly related to the fact that two vaccination rounds were achieved each year. However, it highlights the possible vaccine savings that could be done to compensate the higher vaccination frequency in sub-humid and humid areas. Nevertheless, several pitfalls are encountered with partial vaccination:

vaccine cost is a minor part of the overall vaccination costs encompassing, among other things, wages of veterinary staff, vehicles, gasoline and maintenance of the cold chain. For instance, in a study of vaccination costs for the rinderpest campaign, Ly et al. (1998) reported vaccine represented 23% of the overall costs [44].
Partial vaccination is not easy to implement in actual field conditions: indeed, given the general lack of vaccination pens for small ruminants, kids and lambs have to be sorted and caught one by one by the farmers and vaccinators, thus causing additional work and decreasing the productivity of vaccination teams.
Farmers might be reluctant to only vaccinate the offspring, thus neglecting the adult animals—in particular ewes and nannies, which constitute their productive capital.

Therefore, in practice, we would recommend to implement only full vaccination campaigns.

The PVIR threshold

The PVIR threshold is defined as the population immunity level needed to break the PPRV transmission cycle and to suppress the PPR clinical signs during the PPR control stage [11]. Setting the PVIR to 70% is a somewhat arbitrary, because estimating it at the national level from theoretical considerations—or using numerical simulations, is a difficult task (see below). In this study, we made considerable simplifications by limiting the assessment to small, homogeneous, and PPR-free Epi. U. Some milestones are useful to help assessing the threshold itself.

Firstly, this PVIR threshold assumption is related to T, the control effort to bring the pathogen transmission below the epidemic threshold. In homogeneous populations, T = 1 − 1/R₀, where R₀ is the basic reproduction number, i.e. the number of secondary disease cases after the introduction of as single infectious individual in a fully susceptible population [16]. Therefore, in our simple epidemiological framework, it is crucial to get accurate R₀ estimates to derive adequate values for T. Unfortunately, very few empirical estimates (obtained during PPR outbreaks in field situations) are available [45, 46]. These estimates provided very high values for T (> 80%) but they were obtained in conditions rather different from those encountered in sub-Saharan Africa (animal breeds and density, farming systems, etc.). Furthermore, to our knowledge, no PPRV transmission model has been published so far. Therefore, it is critical to implement systematic PPR outbreak investigation studies, and to promote mathematical modelling work to get reliable and adapted estimates of T.

Regarding this latter topic, coupling the PVIR model with a dynamic PPRV transmission model might allow more precise estimations of the PVIR threshold needed to remain below the epidemic threshold [16]. However, building such a model is a complex task even starting with PPRV transmission in a small Epi. U. under the assumption of homogeneous mixing. Indeed, at last two host species need to be taken into account (sheep and goats) because they are both present in the same herds and villages, with frequent contacts and thus many PPRV transmission opportunities. These species and breeds have specific susceptibilities to PPRV, clinical expression and mortality rates. For instance, West-African dwarf breeds (found in Kolda) are much more susceptible than Sahelian breeds (found in Louga) [1]. Moreover, basic knowledge is scarce regarding important epidemiological parameters, such as the excretion duration of viable PPRV in body fluids and feces [47–49], or the susceptibility of the different small ruminant species and breeds found in sub-Saharan Africa [1]. In addition, PPRV virulence shows some variability [50]. With so many uncertainties, we believe our PVIR-based approach is useful and less prone to problematic assumptions.

Secondly, higher T values (80%) were set in the case of rinderpest control. This threshold was never reached during decades when mass-vaccination campaigns were organized by the Joint Program 15 (JP15), Pan-African rinderpest campaign (PARC), and Pan-African control of epizootics (PACE) eradication programs [10, 51, 52]. Even in a small country like Senegal, with a limited cattle population, only the oldest age classes were close to it after a decade of mass-vaccination campaigns [53]. Nevertheless, rinderpest was finally eradicated, even though the last virus sanctuary was located in remote and unsafe places [54] where the vaccination coverage could not be very high. This might be an indication that for the control stage, the threshold—while useful and important to maintain the motivation of stakeholders, was probably an overestimate of the actual T value, at least for sub-Saharan Africa.

Finally, Morocco was confronted for the first time to the emergence of PPR in 2008. National veterinary services immediately implemented a PPR control strategy based on a 3F1P schedule, followed by a successful eradication stage. During the control stage, more than 80% of the national small ruminant stock (> 20 million heads) was vaccinated each year: pulse vaccination, a single vaccination round per year. In 2012, a nationwide serological survey was implemented and provided an estimate of 70% for PVIR₄₈ in adult ewes [55]. The overall PVIR₄₈ was probably lower than 70%. This is an empirical evidence that in small ruminant farming systems similar to those found in Morocco (mixture of smallholder sheep farms and fattening lots), the actual threshold might even be lower than 70%.

There is a good consistence between the results obtained in this simulation study and the assumptions done when preparing the PPR global control and eradication strategy (GCES) [11] which was designed with the objectives to control and then eradicate PPR at the country, region and global levels, particularly with reference to the choice of the vaccination protocols.

The FAO-OIE PPR GCES uses vaccination as the major tool for combating PPR in endemic countries. Nevertheless, other methods and tools must not be forgotten [56]. Disease surveillance, including field and laboratory work, as well as preparedness for early action to eliminate new PPR outbreaks occurring in free regions or countries.

Together with an appropriate communication strategy, the successful implementation of the GCES also relies on the quality of the animal health services provided to the farmers, particularly by the private and public veterinary services (VS) as well as by other local health stakeholders at the producer levels including community animal health workers [57].

In many sub-Saharan countries, VS efficiency and effectiveness with respect to the farmers must be improved. This is why one of the three components of the FAO-OIE PPR GCES is devoted to strengthening VS in charge of preventing and controlling animal diseases.

Conclusion

This study provided evidences that PPR control should be possible in sub-Saharan Africa. It looks safer to promote the implementation of at least two full mass-vaccination campaigns (2F schedule). In sub-humid and humid areas, partial vaccination (offspring) might provide good epidemiological results, but its practical interest is questionable. Whatever the vaccination schedule and agro-ecological area, the most important feature remains the vaccination coverage and for this purpose, a key aspect is the correct identification of the efficacy of the local socio-technical networks actually providing animal health services to the farmers and especially the quality of VS.

This study was limited to a single, PPR-free Epi. U. The real world is much more complex, with intense livestock movements (trade, transhumance) possibly associated with the introduction of non-immunized animals, or even pathogen agents [58, 59]. Therefore, it is important to maintain the vaccination coverage (introduced animals should be immunized), and to prevent the introduction of the virus once the vaccination has ceased. Thus, the role of the Veterinary Services is crucial both in designing / implementing the vaccination campaigns—together with other stakeholders, and also in other aspects of PPR control, such as recording and accounting for animal mobility. In turn, this information might be used to improve the design of national PPR control program, with a regional and international coordination.

Supporting information

S1 Fig. Annual dynamics of post-vaccination PIR in goat herds, Kolda (Senegal), assuming full vaccination coverage (p = 1).

A total of 144 vaccination scenarios are represented crossing the Tabaski month (12 plots) with the vaccination month (12 lines). On each plot, the origin of the x axis is the vaccination month. The horizontal dotted line represents the 80% protective threshold.

https://doi.org/10.1371/journal.pone.0190296.s001

(TIFF)

S1 Table. Raw data set with individual follow-up in sheep herds of Louga (Senegal).

The file structure and its use are fully described in [60].

https://doi.org/10.1371/journal.pone.0190296.s002

(ZIP)

S2 Table. Raw data set with individual follow-up in goat herds of Kolda (Senegal).

The file structure and its use are fully described in [60].

https://doi.org/10.1371/journal.pone.0190296.s003

(ZIP)

Acknowledgments

This study was conducted in the frame of the project “Vaccine Standard and Pilot Approach for the Progressive Control and Eradication of PPR in Africa” (VSPA), funded by the Bill and Melinda Gates Foundation, the OIE World Animal Health and Welfare Fund, and the French Agricultural Research Centre for International Development (Cirad). Small ruminant demographic data were collected during the joint research programme “Pathologie et Productivité des Petits Ruminants” co-funded by the Institut Sénégalais de Recherches Agricoles (ISRA: Dakar, Senegal), the Directorate of Veterinary Services (DVS: Dakar, Senegal), and Cirad. We warmly thank the farmers, DVS technicians and ISRA technicians, who implemented the field surveys.

References

1. Baron MD, Diallo A, Lancelot R, Libeau G. Peste des Petits Ruminants Virus. vol. 95 of Advances in Virus Research. Kielian M, Maramorosch K, Mettenleiter TC, editors. Academic Press; 2016. Available from: http://www.sciencedirect.com/science/article/pii/S006535271630001X.
2. Lefèvre PC, Blancou J, Chermette R, Uilenberg G, editors. Infectious and parasitic diseases of livestock. Lavoisier Tec & Doc; 2010.
3. Stem C. An economic analysis of the prevention of peste des petits ruminants in Nigerien goats. Preventive Veterinary Medicine. 1993;16(2):141–150.
- View Article
- Google Scholar
4. Diallo A. Control of peste des petits ruminants and poverty alleviation? Journal of Veterinary Medicine, Series B. 2006;53(s1):11–13.
- View Article
- Google Scholar
5. Perry BD, Stones K. Poverty reduction through animal health. Science. 2007;315:333–334. pmid:17234933
- View Article
- PubMed/NCBI
- Google Scholar
6. Alary V, Corniaux C, Gautier D. Livestock’s contribution to poverty alleviation: how to measure it? World Development. 2011;39(9):1638–1648.
- View Article
- Google Scholar
7. Diallo A, Minet C, Le Goff C, Berhe G, Albina E, Libeau G, et al. The threat of peste des petits ruminants: progress in vaccine development for disease control. Vaccine. 2007;25(30):5591–5597. pmid:17399862
- View Article
- PubMed/NCBI
- Google Scholar
8. Buczkowski H, Muniraju M, Parida S, Banyard AC. Morbillivirus vaccines: Recent successes and future hopes. Vaccine. 2014;32(26):3155–3161. pmid:24703852
- View Article
- PubMed/NCBI
- Google Scholar
9. Liu F, Wu X, Liu W, Li L, Wang Z. Current perspectives on conventional and novel vaccines against peste des petits ruminants. Veterinary Research Communications. 2014;38(4):307–322. pmid:25224755
- View Article
- PubMed/NCBI
- Google Scholar
10. Roeder P, Mariner J, Kock R. Rinderpest: the veterinary perspective on eradication. Philosophical Transactions of the Royal Society of London B: Biological Sciences. 2013;368 (1623):20120139. pmid:23798687
- View Article
- PubMed/NCBI
- Google Scholar
11. FAO, OIE. Global strategy for the progressive control and eradication of PPR. Paris, Rome: FAO and OIE; 2015. Available from: http://www.oie.int/fr/PPR2015/doc/PPR-Global-Strategy-avecAnnexes_2015-03-28.pdf.
12. Kairu-Wanyoike SW, Kaitibie S, Heffernan C, Taylor NM, Gitau GK, Kiara H, et al. Willingness to pay for contagious bovine pleuropneumonia vaccination in Narok South District of Kenya. Preventive Veterinary Medicine. 2014;115(3–4):130–142. pmid:24774477
- View Article
- PubMed/NCBI
- Google Scholar
13. Abubakar M, Arshed MJ, Zahur AB, Ali Q, Banyard AC. Natural infection with peste des petits ruminants virus: a pre and post vaccinal assessment following an outbreak scenario. Virus research. 2012;167(1):43–47. pmid:22504337
- View Article
- PubMed/NCBI
- Google Scholar
14. El-Yuguda AD, Baba SS, Ambali AG, Egwu GO. Field Trial of a Thermostable Peste des petits ruminants (PPR) Vaccine in a Semi-Arid Zone of Nigeria. World Journal of Vaccines. 2014;04(01):1–6.
- View Article
- Google Scholar
15. Abubakar M, Manzoor S, Ali Q. Evaluating the role of vaccine to combat peste des petits ruminants outbreaks in endemic disease situation. J Anim Sci Technol. 2015;57:2. pmid:26290722
- View Article
- PubMed/NCBI
- Google Scholar
16. Heesterbeek JAP, Roberts MG. The type-reproduction number T in models for infectious disease control. Mathematical Biosciences. 2007;206(1):3–10. pmid:16529777
- View Article
- PubMed/NCBI
- Google Scholar
17. Lesnoff M, Peyre M, Duarte P, Renard JF, Mariner J. A simple model for simulating immunity rate dynamics in a tropical free-range poultry population after avian influenza vaccination. Epidemiology and infection. 2009;137(10):1405–1413. pmid:19327199
- View Article
- PubMed/NCBI
- Google Scholar
18. Hammami P, Lancelot R, Lesnoff M. Modelling the Dynamics of Post-Vaccination Immunity Rate in a Population of Sahelian Sheep after a Vaccination Campaign against Peste des Petits Ruminants Virus. PLOS ONE. 2016;11:e0161769. pmid:27603710
- View Article
- PubMed/NCBI
- Google Scholar
19. Faugère O, Dockes A, Perrot C, Faugère B. Traditional small ruminant rearing system in Senegal. II. Animal management and husbandry pratices by livestock owners in the Louga area. Revue d’Elevage et de Médecine vétérinaire des Pays Tropicaux. 1990;43(2).
- View Article
- Google Scholar
20. Faugère O, Dockes A, Perrot C, Faugère B, et al. Traditional breeding of small ruminants in Senegal. I. Animal management and breeding practices at farms in Kolda region. Revue d’Élevage et de Médecine Vétérinaire des Pays Tropicaux. 1990;43(2):249–259.
- View Article
- Google Scholar
21. Robinson TP, Wint GRW, Conchedda G, Van Boeckel TP, Ercoli V, Palamara E, et al. Mapping the Global Distribution of Livestock. PLOS ONE. 2014;9(5):e96084. pmid:24875496
- View Article
- PubMed/NCBI
- Google Scholar
22. El Arbi AS, El Mamy AB, Salami H, Isselmou E, Kwiatek O, Libeau G, et al. Peste des petits ruminants virus, Mauritania. Emerging infectious diseases. 2014;20(2):334.
- View Article
- Google Scholar
23. Caswell H. Matrix population models. Wiley Online Library; 2001.
24. Lancelot R, Faye B, Juanès X, Ndiaye M, Perochon L, Tillard E. La base de données BAOBAB: un outil pour modéliser la production et la santé des petits ruminants dans les systèmes d’élevage traditionnels au Sénégal. Rev Elev Méd Vét Pays Trop. 1998;51(2):135–146.
- View Article
- Google Scholar
25. Zomer R, Trabucco A, van Straaten O, Bossio D. Carbon, land and water: A global analysis of the hydrologic dimensions of climate change mitigation through afforestation/reforestation. International Water Management Institute; 2006.
26. Trabucco A, Zomer R. Global aridity index (global-aridity) and global potential evapo-transpiration (global-PET) geospatial database. CGIAR Consortium for Spatial Information Published online, available from the CGIAR-CSI GeoPortal at: http://www.csi.cgiar.org/ (2009) Global Aridity Index (Global-Aridity) and Global Potential Evapo-Transpiration (Global-PET) Geospatial Database In CGIAR Consortium for Spatial Information. 2009;.
27. Clément V, Poivey J, Faugère O, Tillard E, Lancelot R, Gueye A, et al. Etude de la variabilité des caractères de reproduction chez les petits ruminants en milieu d’élevage traditionnel au Sénégal. Revue d’Elevage et de Médecine véterinaire des Pays Tropicaux. 1997;50(3):235–249.
- View Article
- Google Scholar
28. Lesnoff M. Dynamics of a sheep population in a Sahelian area (Ndiagne district in Senegal): a periodic matrix model. Agricultural Systems. 1999;61(3):207–221.
- View Article
- Google Scholar
29. Goossens B, Osaer S, Kora S, Jaitner J, Ndao M, Geerts S. The interaction of Trypanosoma congolense and Haemonchus contortus in Djallonke sheep. International Journal for Parasitology. 1997;27(12):1579–1584. pmid:9467745
- View Article
- PubMed/NCBI
- Google Scholar
30. Zahur AB, Irshad H, Ullah A, Afzal M, Latif A, Ullah RW, et al. Peste des Petits Ruminants Vaccine (Nigerian Strain 75/1) Confers Protection for at Least 3 Years in Sheep and Goats. Journal of Biosciences and Medicines. 2014;02(06):27–33.
- View Article
- Google Scholar
31. Ata F, Al Sumry H, King G, Ismaili S, Ata A. Duration of maternal immunity to peste des petits ruminants. Veterinary Record. 1989;124(22):590–591. pmid:2773199
- View Article
- PubMed/NCBI
- Google Scholar
32. Bidjeh K, Diguimbaye C, Hendrikx P, Dedet V, Tchari D, et al. Immunité passive colostrale chez les jeunes issus des chèvres et brebis vaccinées avec le vaccin antipeste des petits ruminants. Cahiers Agricultures. 1999;8(3):219–222.
- View Article
- Google Scholar
33. Awa DN, Ngagnou A, Tefiang E, Yaya D, Njoya A. Post vaccination and colostral peste des petits ruminants antibody dynamics in research flocks of Kirdi goats and Foulbe sheep of north Cameroon. Preventive veterinary medicine. 2002;55(4):265–271. pmid:12392877
- View Article
- PubMed/NCBI
- Google Scholar
34. Bodjo SC, Couacy-Hymann E, Koffi MY, Danho T. Assessment of the duration of maternal antibodies specific to the homologous peste des petits ruminant vaccine “Nigeria 75/1” in Djallonké lambs. Biokemistri. 2006;18(2).
- View Article
- Google Scholar
35. Niewiesk S. Maternal antibodies: clinical significance, mechanism of interference with immune responses, and possible vaccination strategies. Frontiers in immunology. 2014;5. pmid:25278941
- View Article
- PubMed/NCBI
- Google Scholar
36. McCullagh P, Nelder JA. Generalized linear models. vol. 37. CRC press; 1989.
37. Yoccoz NG. Use, overuse, and misuse of significance tests in evolutionary biology and ecology. Bulletin of the Ecological Society of America. 1991;72:106–111.
- View Article
- Google Scholar
38. Texeira M, Paruelo JM, Jobbagy E. How do forage availability and climate control sheep reproductive performance? An analysis based on artificial neural networks and remotely sensed data. Ecological Modelling. 2008;217(1–2):197–206.
- View Article
- Google Scholar
39. Anyamba A, Small JL, Tucker CJ, Pak EW. Thirty-two years of Sahelian zone growing season non-stationary NDVI3g patterns and trends. Remote Sensing. 2014;6(4):3101–3122.
- View Article
- Google Scholar
40. Balamurugan V, Sen A, Venkatesan G, Rajak KK, Bhanuprakash V, Singh RK. Study on passive immunity: Time of vaccination in kids born to goats vaccinated against Peste des petits ruminants. Virologica Sinica. 2012;27(4):228–233. pmid:22899430
- View Article
- PubMed/NCBI
- Google Scholar
41. Moulin CH. Animal performance and breeding practices in Sahelian Africa: diversity of functionning in small ruminant herds in the rural community of Ndiagne (Senegal). Institut National Agronomique Paris-Grignon; 1993.
42. Al-Sabbagh T, Swanson L, Thompson J. The effect of ewe body condition at lambing on colostral immunoglobulin G concentration and lamb performance. Journal of animal science. 1995;73(10):2860–2864. pmid:8617654
- View Article
- PubMed/NCBI
- Google Scholar
43. Hashemi M, Zamiri M, Safdarian M. Effects of nutritional level during late pregnancy on colostral production and blood immunoglobulin levels of Karakul ewes and their lambs. Small Ruminant Research. 2008;75(2):204–209.
- View Article
- Google Scholar
44. Ly C, Kane I, Diop B, Akakpo A. Analyse des coûts d’une campagne officielle de prophylaxie animale l’exemple du Sénégal en 1996 [Cost analysis of an official campaign for animal disease control: the example of Senegal in 1996]. Revue Scientifique et Technique, Office International des Epizooties. 1998;17(3):767–776.
- View Article
- Google Scholar
45. Zahur A, Ullah A, Irshad H, Farooq M, Hussain M, Jahangir M, et al. Epidemiological investigations of a peste des petits ruminants (PPR) outbreak in Afghan sheep in Pakistan. Pakistan Veterinary Journal. 2009;29(4):174–178.
- View Article
- Google Scholar
46. Kivaria FM, Kwiatek O, Kapaga AM, Swai ES, Libeau G, Moshy W, et al. The incursion, persistence and spread of peste des petits ruminants in Tanzania: Epidemiological patterns and predictions. Onderstepoort Journal of Veterinary Research. 2013;80(1):01–10.
- View Article
- Google Scholar
47. Liu W, Wu X, Wang Z, Bao J, Li L, Zhao Y, et al. Virus excretion and antibody dynamics in goats inoculated with a field isolate of peste des petits ruminants virus. Transboundary and emerging diseases. 2013;60 Suppl 2:63–68. pmid:24589103
- View Article
- PubMed/NCBI
- Google Scholar
48. Wernike K, Eschbaumer M, Breithaupt A, Maltzan J, Wiesner H, Beer M, et al. Experimental infection of sheep and goats with a recent isolate of peste des petits ruminants virus from Kurdistan. Vet Microbiol. 2014;172(1–2):140–145. pmid:24908276
- View Article
- PubMed/NCBI
- Google Scholar
49. Cosseddu G, Polci A, Pinoni C, Capobianco Dondona A, Iapaolo F, Orsini G, et al. Evaluation of Humoral Response and Protective Efficacy of an Inactivated Vaccine Against Peste des Petits Ruminants Virus in Goats. Transbound Emerg Dis. 2016;63(5):e447–e452. pmid:25594237
- View Article
- PubMed/NCBI
- Google Scholar
50. Couacy-Hymann E, Bodjo C, Danho T, Libeau G, Diallo A. Evaluation of the virulence of some strains of peste-des-petits-ruminants virus (PPRV) in experimentally infected West African dwarf goats. Veterinary Journal. 2007;173(1):178–183.
- View Article
- Google Scholar
51. Provost A. Bases scientifiques et techniques de l’éradication de la peste bovine en Afrique intertropicale. Revue Scientifique et Technique de l’OIE. 1982;1(3):589–618.
- View Article
- Google Scholar
52. Geiger R, editor. The seromonitoring and surveillance of rinderpest throughout Africa. Phase III. Results for 1998. Proceedings of a Research Co-ordination Meeting of the FAO/IAEA/OAU/IBAR/PARC Co-ordinated Research Project organized by the Joint FAO/IAEA Division of Nuclear Techniques in Food and Agriculture, held in Machakos, Kenya, 26–30 April 1999. Vienna, Austria: IAEA/FAO; 1999.
53. Sarr J, Diop M. Rinderpest in Senegal: Results of two years of seromonitoring 1991-1992. In: Jeggo RE M & Geiger, editor. Research Co-ordination Meeting of the FAO//AEA/SIDA/OAU/IBAR/PARC Co-ordinated Research Programme organized by the Joint FAO!IAEA Division of Nuclear Techniques in Food and Agriculture and held in Cairo, Egypt, 7-11 November 1993; 1994. p. 195–204.
54. Mariner JC, House JA, Mebus CA, Sollod AE, Chibeu D, Jones BA, et al. Rinderpest eradication: appropriate technology and social innovations. Science. 2012;337 (6100):1309–1312. pmid:22984063
- View Article
- PubMed/NCBI
- Google Scholar
55. Ettair M. Stratégie de surveillance et de lutte contre la PPR au Maroc. In: REMESA: atelier conjoint REPIVET-RESEPSA des 12 et 13 Juillet 2012. Tunis; 2012. p. 1–15.
56. Albina E, Kwiatek O, Minet C, Lancelot R, Servan de Almeida R, Libeau G. Peste des petits ruminants, the next eradicated animal disease? Veterinary microbiology. 2013;165(1):38–44. pmid:23313537
- View Article
- PubMed/NCBI
- Google Scholar
57. Lancelot R, Bouyer F, Peyre M, Chavernac D, Hammami P, Kwiatek O, et al. Vaccine Standards and Pilot Approach to PPR Control in Africa (VSPA). Component 3: epidemiological assessment of PPR vaccination strategies in Burkina Faso and Ghana. Montpellier: Cirad / OIE; 2014.
58. Cêtre-Sossah C, Kwiatek O, Faharoudine A, Soulé M, Moutroifi Y, Vrel M, et al. Impact and epidemiological investigations into the incursion and spread of peste des petits ruminants in the Comoros archipelago: an increased threat to surrounding islands. Transbound Emerg Dis. 2014;.
- View Article
- Google Scholar
59. Lancelot R, Béral M, Rakotoharinome VM, Andriamandimby SF, Héraud JM, Coste C, et al. Drivers of Rift Valley fever epidemics in Madagascar. Proceedings of the National Academy of Sciences of the United States of America. 2017;114(5):938–943. pmid:28096420
- View Article
- PubMed/NCBI
- Google Scholar
60. Lesnoff M, Lancelot R, Moulin C, Messad S, Juanés X, Sahut C. Calculation of demographic parameters in tropical livestock herds: A discrete time approach with LASER animal-based monitoring data. Springer, Dordrecht. 2014.

[ref1] 1. Baron MD, Diallo A, Lancelot R, Libeau G. Peste des Petits Ruminants Virus. vol. 95 of Advances in Virus Research. Kielian M, Maramorosch K, Mettenleiter TC, editors. Academic Press; 2016. Available from: http://www.sciencedirect.com/science/article/pii/S006535271630001X.

[ref2] 2. Lefèvre PC, Blancou J, Chermette R, Uilenberg G, editors. Infectious and parasitic diseases of livestock. Lavoisier Tec & Doc; 2010.

[ref3] 3. Stem C. An economic analysis of the prevention of peste des petits ruminants in Nigerien goats. Preventive Veterinary Medicine. 1993;16(2):141–150.
View Article
Google Scholar

[4] View Article

[5] Google Scholar

[ref4] 4. Diallo A. Control of peste des petits ruminants and poverty alleviation? Journal of Veterinary Medicine, Series B. 2006;53(s1):11–13.
View Article
Google Scholar

[7] View Article

[8] Google Scholar

[ref5] 5. Perry BD, Stones K. Poverty reduction through animal health. Science. 2007;315:333–334. pmid:17234933
View Article
PubMed/NCBI
Google Scholar

[10] View Article

[11] PubMed/NCBI

[12] Google Scholar

[ref6] 6. Alary V, Corniaux C, Gautier D. Livestock’s contribution to poverty alleviation: how to measure it? World Development. 2011;39(9):1638–1648.
View Article
Google Scholar

[14] View Article

[15] Google Scholar

[ref7] 7. Diallo A, Minet C, Le Goff C, Berhe G, Albina E, Libeau G, et al. The threat of peste des petits ruminants: progress in vaccine development for disease control. Vaccine. 2007;25(30):5591–5597. pmid:17399862
View Article
PubMed/NCBI
Google Scholar

[17] View Article

[18] PubMed/NCBI

[19] Google Scholar

[ref8] 8. Buczkowski H, Muniraju M, Parida S, Banyard AC. Morbillivirus vaccines: Recent successes and future hopes. Vaccine. 2014;32(26):3155–3161. pmid:24703852
View Article
PubMed/NCBI
Google Scholar

[21] View Article

[22] PubMed/NCBI

[23] Google Scholar

[ref9] 9. Liu F, Wu X, Liu W, Li L, Wang Z. Current perspectives on conventional and novel vaccines against peste des petits ruminants. Veterinary Research Communications. 2014;38(4):307–322. pmid:25224755
View Article
PubMed/NCBI
Google Scholar

[25] View Article

[26] PubMed/NCBI

[27] Google Scholar

[ref10] 10. Roeder P, Mariner J, Kock R. Rinderpest: the veterinary perspective on eradication. Philosophical Transactions of the Royal Society of London B: Biological Sciences. 2013;368 (1623):20120139. pmid:23798687
View Article
PubMed/NCBI
Google Scholar

[29] View Article

[30] PubMed/NCBI

[31] Google Scholar

[ref11] 11. FAO, OIE. Global strategy for the progressive control and eradication of PPR. Paris, Rome: FAO and OIE; 2015. Available from: http://www.oie.int/fr/PPR2015/doc/PPR-Global-Strategy-avecAnnexes_2015-03-28.pdf.

[ref12] 12. Kairu-Wanyoike SW, Kaitibie S, Heffernan C, Taylor NM, Gitau GK, Kiara H, et al. Willingness to pay for contagious bovine pleuropneumonia vaccination in Narok South District of Kenya. Preventive Veterinary Medicine. 2014;115(3–4):130–142. pmid:24774477
View Article
PubMed/NCBI
Google Scholar

[34] View Article

[35] PubMed/NCBI

[36] Google Scholar

[ref13] 13. Abubakar M, Arshed MJ, Zahur AB, Ali Q, Banyard AC. Natural infection with peste des petits ruminants virus: a pre and post vaccinal assessment following an outbreak scenario. Virus research. 2012;167(1):43–47. pmid:22504337
View Article
PubMed/NCBI
Google Scholar

[38] View Article

[39] PubMed/NCBI

[40] Google Scholar

[ref14] 14. El-Yuguda AD, Baba SS, Ambali AG, Egwu GO. Field Trial of a Thermostable Peste des petits ruminants (PPR) Vaccine in a Semi-Arid Zone of Nigeria. World Journal of Vaccines. 2014;04(01):1–6.
View Article
Google Scholar

[42] View Article

[43] Google Scholar

[ref15] 15. Abubakar M, Manzoor S, Ali Q. Evaluating the role of vaccine to combat peste des petits ruminants outbreaks in endemic disease situation. J Anim Sci Technol. 2015;57:2. pmid:26290722
View Article
PubMed/NCBI
Google Scholar

[45] View Article

[46] PubMed/NCBI

[47] Google Scholar

[ref16] 16. Heesterbeek JAP, Roberts MG. The type-reproduction number T in models for infectious disease control. Mathematical Biosciences. 2007;206(1):3–10. pmid:16529777
View Article
PubMed/NCBI
Google Scholar

[49] View Article

[50] PubMed/NCBI

[51] Google Scholar

[ref17] 17. Lesnoff M, Peyre M, Duarte P, Renard JF, Mariner J. A simple model for simulating immunity rate dynamics in a tropical free-range poultry population after avian influenza vaccination. Epidemiology and infection. 2009;137(10):1405–1413. pmid:19327199
View Article
PubMed/NCBI
Google Scholar

[53] View Article

[54] PubMed/NCBI

[55] Google Scholar

[ref18] 18. Hammami P, Lancelot R, Lesnoff M. Modelling the Dynamics of Post-Vaccination Immunity Rate in a Population of Sahelian Sheep after a Vaccination Campaign against Peste des Petits Ruminants Virus. PLOS ONE. 2016;11:e0161769. pmid:27603710
View Article
PubMed/NCBI
Google Scholar

[57] View Article

[58] PubMed/NCBI

[59] Google Scholar

[ref19] 19. Faugère O, Dockes A, Perrot C, Faugère B. Traditional small ruminant rearing system in Senegal. II. Animal management and husbandry pratices by livestock owners in the Louga area. Revue d’Elevage et de Médecine vétérinaire des Pays Tropicaux. 1990;43(2).
View Article
Google Scholar

[61] View Article

[62] Google Scholar

[ref20] 20. Faugère O, Dockes A, Perrot C, Faugère B, et al. Traditional breeding of small ruminants in Senegal. I. Animal management and breeding practices at farms in Kolda region. Revue d’Élevage et de Médecine Vétérinaire des Pays Tropicaux. 1990;43(2):249–259.
View Article
Google Scholar

[64] View Article

[65] Google Scholar

[ref21] 21. Robinson TP, Wint GRW, Conchedda G, Van Boeckel TP, Ercoli V, Palamara E, et al. Mapping the Global Distribution of Livestock. PLOS ONE. 2014;9(5):e96084. pmid:24875496
View Article
PubMed/NCBI
Google Scholar

[67] View Article

[68] PubMed/NCBI

[69] Google Scholar

[ref22] 22. El Arbi AS, El Mamy AB, Salami H, Isselmou E, Kwiatek O, Libeau G, et al. Peste des petits ruminants virus, Mauritania. Emerging infectious diseases. 2014;20(2):334.
View Article
Google Scholar

[71] View Article

[72] Google Scholar

[ref23] 23. Caswell H. Matrix population models. Wiley Online Library; 2001.

[ref24] 24. Lancelot R, Faye B, Juanès X, Ndiaye M, Perochon L, Tillard E. La base de données BAOBAB: un outil pour modéliser la production et la santé des petits ruminants dans les systèmes d’élevage traditionnels au Sénégal. Rev Elev Méd Vét Pays Trop. 1998;51(2):135–146.
View Article
Google Scholar

[75] View Article

[76] Google Scholar

[ref25] 25. Zomer R, Trabucco A, van Straaten O, Bossio D. Carbon, land and water: A global analysis of the hydrologic dimensions of climate change mitigation through afforestation/reforestation. International Water Management Institute; 2006.

[ref26] 26. Trabucco A, Zomer R. Global aridity index (global-aridity) and global potential evapo-transpiration (global-PET) geospatial database. CGIAR Consortium for Spatial Information Published online, available from the CGIAR-CSI GeoPortal at: http://www.csi.cgiar.org/ (2009) Global Aridity Index (Global-Aridity) and Global Potential Evapo-Transpiration (Global-PET) Geospatial Database In CGIAR Consortium for Spatial Information. 2009;.

[ref27] 27. Clément V, Poivey J, Faugère O, Tillard E, Lancelot R, Gueye A, et al. Etude de la variabilité des caractères de reproduction chez les petits ruminants en milieu d’élevage traditionnel au Sénégal. Revue d’Elevage et de Médecine véterinaire des Pays Tropicaux. 1997;50(3):235–249.
View Article
Google Scholar

[80] View Article

[81] Google Scholar

[ref28] 28. Lesnoff M. Dynamics of a sheep population in a Sahelian area (Ndiagne district in Senegal): a periodic matrix model. Agricultural Systems. 1999;61(3):207–221.
View Article
Google Scholar

[83] View Article

[84] Google Scholar

[ref29] 29. Goossens B, Osaer S, Kora S, Jaitner J, Ndao M, Geerts S. The interaction of Trypanosoma congolense and Haemonchus contortus in Djallonke sheep. International Journal for Parasitology. 1997;27(12):1579–1584. pmid:9467745
View Article
PubMed/NCBI
Google Scholar

[86] View Article

[87] PubMed/NCBI

[88] Google Scholar

[ref30] 30. Zahur AB, Irshad H, Ullah A, Afzal M, Latif A, Ullah RW, et al. Peste des Petits Ruminants Vaccine (Nigerian Strain 75/1) Confers Protection for at Least 3 Years in Sheep and Goats. Journal of Biosciences and Medicines. 2014;02(06):27–33.
View Article
Google Scholar

[90] View Article

[91] Google Scholar

[ref31] 31. Ata F, Al Sumry H, King G, Ismaili S, Ata A. Duration of maternal immunity to peste des petits ruminants. Veterinary Record. 1989;124(22):590–591. pmid:2773199
View Article
PubMed/NCBI
Google Scholar

[93] View Article

[94] PubMed/NCBI

[95] Google Scholar

[ref32] 32. Bidjeh K, Diguimbaye C, Hendrikx P, Dedet V, Tchari D, et al. Immunité passive colostrale chez les jeunes issus des chèvres et brebis vaccinées avec le vaccin antipeste des petits ruminants. Cahiers Agricultures. 1999;8(3):219–222.
View Article
Google Scholar

[97] View Article

[98] Google Scholar

[ref33] 33. Awa DN, Ngagnou A, Tefiang E, Yaya D, Njoya A. Post vaccination and colostral peste des petits ruminants antibody dynamics in research flocks of Kirdi goats and Foulbe sheep of north Cameroon. Preventive veterinary medicine. 2002;55(4):265–271. pmid:12392877
View Article
PubMed/NCBI
Google Scholar

[100] View Article

[101] PubMed/NCBI

[102] Google Scholar

[ref34] 34. Bodjo SC, Couacy-Hymann E, Koffi MY, Danho T. Assessment of the duration of maternal antibodies specific to the homologous peste des petits ruminant vaccine “Nigeria 75/1” in Djallonké lambs. Biokemistri. 2006;18(2).
View Article
Google Scholar

[104] View Article

[105] Google Scholar

[ref35] 35. Niewiesk S. Maternal antibodies: clinical significance, mechanism of interference with immune responses, and possible vaccination strategies. Frontiers in immunology. 2014;5. pmid:25278941
View Article
PubMed/NCBI
Google Scholar

[107] View Article

[108] PubMed/NCBI

[109] Google Scholar

[ref36] 36. McCullagh P, Nelder JA. Generalized linear models. vol. 37. CRC press; 1989.

[ref37] 37. Yoccoz NG. Use, overuse, and misuse of significance tests in evolutionary biology and ecology. Bulletin of the Ecological Society of America. 1991;72:106–111.
View Article
Google Scholar

[112] View Article

[113] Google Scholar

[ref38] 38. Texeira M, Paruelo JM, Jobbagy E. How do forage availability and climate control sheep reproductive performance? An analysis based on artificial neural networks and remotely sensed data. Ecological Modelling. 2008;217(1–2):197–206.
View Article
Google Scholar

[115] View Article

[116] Google Scholar

[ref39] 39. Anyamba A, Small JL, Tucker CJ, Pak EW. Thirty-two years of Sahelian zone growing season non-stationary NDVI3g patterns and trends. Remote Sensing. 2014;6(4):3101–3122.
View Article
Google Scholar

[118] View Article

[119] Google Scholar

[ref40] 40. Balamurugan V, Sen A, Venkatesan G, Rajak KK, Bhanuprakash V, Singh RK. Study on passive immunity: Time of vaccination in kids born to goats vaccinated against Peste des petits ruminants. Virologica Sinica. 2012;27(4):228–233. pmid:22899430
View Article
PubMed/NCBI
Google Scholar

[121] View Article

[122] PubMed/NCBI

[123] Google Scholar

[ref41] 41. Moulin CH. Animal performance and breeding practices in Sahelian Africa: diversity of functionning in small ruminant herds in the rural community of Ndiagne (Senegal). Institut National Agronomique Paris-Grignon; 1993.

[ref42] 42. Al-Sabbagh T, Swanson L, Thompson J. The effect of ewe body condition at lambing on colostral immunoglobulin G concentration and lamb performance. Journal of animal science. 1995;73(10):2860–2864. pmid:8617654
View Article
PubMed/NCBI
Google Scholar

[126] View Article

[127] PubMed/NCBI

[128] Google Scholar

[ref43] 43. Hashemi M, Zamiri M, Safdarian M. Effects of nutritional level during late pregnancy on colostral production and blood immunoglobulin levels of Karakul ewes and their lambs. Small Ruminant Research. 2008;75(2):204–209.
View Article
Google Scholar

[130] View Article

[131] Google Scholar

[ref44] 44. Ly C, Kane I, Diop B, Akakpo A. Analyse des coûts d’une campagne officielle de prophylaxie animale l’exemple du Sénégal en 1996 [Cost analysis of an official campaign for animal disease control: the example of Senegal in 1996]. Revue Scientifique et Technique, Office International des Epizooties. 1998;17(3):767–776.
View Article
Google Scholar

[133] View Article

[134] Google Scholar

[ref45] 45. Zahur A, Ullah A, Irshad H, Farooq M, Hussain M, Jahangir M, et al. Epidemiological investigations of a peste des petits ruminants (PPR) outbreak in Afghan sheep in Pakistan. Pakistan Veterinary Journal. 2009;29(4):174–178.
View Article
Google Scholar

[136] View Article

[137] Google Scholar

[ref46] 46. Kivaria FM, Kwiatek O, Kapaga AM, Swai ES, Libeau G, Moshy W, et al. The incursion, persistence and spread of peste des petits ruminants in Tanzania: Epidemiological patterns and predictions. Onderstepoort Journal of Veterinary Research. 2013;80(1):01–10.
View Article
Google Scholar

[139] View Article

[140] Google Scholar

[ref47] 47. Liu W, Wu X, Wang Z, Bao J, Li L, Zhao Y, et al. Virus excretion and antibody dynamics in goats inoculated with a field isolate of peste des petits ruminants virus. Transboundary and emerging diseases. 2013;60 Suppl 2:63–68. pmid:24589103
View Article
PubMed/NCBI
Google Scholar

[142] View Article

[143] PubMed/NCBI

[144] Google Scholar

[ref48] 48. Wernike K, Eschbaumer M, Breithaupt A, Maltzan J, Wiesner H, Beer M, et al. Experimental infection of sheep and goats with a recent isolate of peste des petits ruminants virus from Kurdistan. Vet Microbiol. 2014;172(1–2):140–145. pmid:24908276
View Article
PubMed/NCBI
Google Scholar

[146] View Article

[147] PubMed/NCBI

[148] Google Scholar

[ref49] 49. Cosseddu G, Polci A, Pinoni C, Capobianco Dondona A, Iapaolo F, Orsini G, et al. Evaluation of Humoral Response and Protective Efficacy of an Inactivated Vaccine Against Peste des Petits Ruminants Virus in Goats. Transbound Emerg Dis. 2016;63(5):e447–e452. pmid:25594237
View Article
PubMed/NCBI
Google Scholar

[150] View Article

[151] PubMed/NCBI

[152] Google Scholar

[ref50] 50. Couacy-Hymann E, Bodjo C, Danho T, Libeau G, Diallo A. Evaluation of the virulence of some strains of peste-des-petits-ruminants virus (PPRV) in experimentally infected West African dwarf goats. Veterinary Journal. 2007;173(1):178–183.
View Article
Google Scholar

[154] View Article

[155] Google Scholar

[ref51] 51. Provost A. Bases scientifiques et techniques de l’éradication de la peste bovine en Afrique intertropicale. Revue Scientifique et Technique de l’OIE. 1982;1(3):589–618.
View Article
Google Scholar

[157] View Article

[158] Google Scholar

[ref52] 52. Geiger R, editor. The seromonitoring and surveillance of rinderpest throughout Africa. Phase III. Results for 1998. Proceedings of a Research Co-ordination Meeting of the FAO/IAEA/OAU/IBAR/PARC Co-ordinated Research Project organized by the Joint FAO/IAEA Division of Nuclear Techniques in Food and Agriculture, held in Machakos, Kenya, 26–30 April 1999. Vienna, Austria: IAEA/FAO; 1999.

[ref53] 53. Sarr J, Diop M. Rinderpest in Senegal: Results of two years of seromonitoring 1991-1992. In: Jeggo RE M & Geiger, editor. Research Co-ordination Meeting of the FAO//AEA/SIDA/OAU/IBAR/PARC Co-ordinated Research Programme organized by the Joint FAO!IAEA Division of Nuclear Techniques in Food and Agriculture and held in Cairo, Egypt, 7-11 November 1993; 1994. p. 195–204.

[ref54] 54. Mariner JC, House JA, Mebus CA, Sollod AE, Chibeu D, Jones BA, et al. Rinderpest eradication: appropriate technology and social innovations. Science. 2012;337 (6100):1309–1312. pmid:22984063
View Article
PubMed/NCBI
Google Scholar

[162] View Article

[163] PubMed/NCBI

[164] Google Scholar

[ref55] 55. Ettair M. Stratégie de surveillance et de lutte contre la PPR au Maroc. In: REMESA: atelier conjoint REPIVET-RESEPSA des 12 et 13 Juillet 2012. Tunis; 2012. p. 1–15.

[ref56] 56. Albina E, Kwiatek O, Minet C, Lancelot R, Servan de Almeida R, Libeau G. Peste des petits ruminants, the next eradicated animal disease? Veterinary microbiology. 2013;165(1):38–44. pmid:23313537
View Article
PubMed/NCBI
Google Scholar

[167] View Article

[168] PubMed/NCBI

[169] Google Scholar

[ref57] 57. Lancelot R, Bouyer F, Peyre M, Chavernac D, Hammami P, Kwiatek O, et al. Vaccine Standards and Pilot Approach to PPR Control in Africa (VSPA). Component 3: epidemiological assessment of PPR vaccination strategies in Burkina Faso and Ghana. Montpellier: Cirad / OIE; 2014.

[ref58] 58. Cêtre-Sossah C, Kwiatek O, Faharoudine A, Soulé M, Moutroifi Y, Vrel M, et al. Impact and epidemiological investigations into the incursion and spread of peste des petits ruminants in the Comoros archipelago: an increased threat to surrounding islands. Transbound Emerg Dis. 2014;.
View Article
Google Scholar

[172] View Article

[173] Google Scholar

[ref59] 59. Lancelot R, Béral M, Rakotoharinome VM, Andriamandimby SF, Héraud JM, Coste C, et al. Drivers of Rift Valley fever epidemics in Madagascar. Proceedings of the National Academy of Sciences of the United States of America. 2017;114(5):938–943. pmid:28096420
View Article
PubMed/NCBI
Google Scholar

[175] View Article

[176] PubMed/NCBI

[177] Google Scholar

[ref60] 60. Lesnoff M, Lancelot R, Moulin C, Messad S, Juanés X, Sahut C. Calculation of demographic parameters in tropical livestock herds: A discrete time approach with LASER animal-based monitoring data. Springer, Dordrecht. 2014.

Figures

Abstract

Background

Methods

Results

Introduction

Materials and methods

Small ruminant farming systems

Design for the PVIR assessment

Assessment method

Data analysis

Results

Overview

The reference vaccination schedule 2F2P

Relative difference with respect to the 2F2P vaccination schedule

Discussion

Validity of the small ruminant demographic dataset

Vaccination month

Vaccination coverage and vaccination schedule

The PVIR threshold

Conclusion

Supporting information

S1 Fig. Annual dynamics of post-vaccination PIR in goat herds, Kolda (Senegal), assuming full vaccination coverage (p = 1).

S1 Table. Raw data set with individual follow-up in sheep herds of Louga (Senegal).

S2 Table. Raw data set with individual follow-up in goat herds of Kolda (Senegal).

Acknowledgments

References